Overview

Dataset statistics

Number of variables36
Number of observations858
Missing cells0
Missing cells (%)0.0%
Duplicate rows20
Duplicate rows (%)2.3%
Total size in memory241.4 KiB
Average record size in memory288.1 B

Variable types

Numeric6
Categorical29
Text1

Alerts

Dataset has 20 (2.3%) duplicate rowsDuplicates
Smokes (years) is highly overall correlated with SmokesHigh correlation
Smokes is highly overall correlated with Smokes (years)High correlation
Hormonal Contraceptives is highly overall correlated with IUD and 15 other fieldsHigh correlation
IUD is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
IUD (years) is highly overall correlated with Hormonal Contraceptives and 14 other fieldsHigh correlation
STDs is highly overall correlated with Hormonal Contraceptives and 18 other fieldsHigh correlation
STDs (number) is highly overall correlated with Hormonal Contraceptives and 16 other fieldsHigh correlation
STDs:condylomatosis is highly overall correlated with Hormonal Contraceptives and 17 other fieldsHigh correlation
STDs:cervical condylomatosis is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:vaginal condylomatosis is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:vulvo-perineal condylomatosis is highly overall correlated with Hormonal Contraceptives and 17 other fieldsHigh correlation
STDs:syphilis is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:pelvic inflammatory disease is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:genital herpes is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:molluscum contagiosum is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:AIDS is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:HIV is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:Hepatitis B is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs:HPV is highly overall correlated with Hormonal Contraceptives and 15 other fieldsHigh correlation
STDs: Number of diagnosis is highly overall correlated with STDs and 3 other fieldsHigh correlation
STDs: Time since first diagnosis is highly overall correlated with STDs and 5 other fieldsHigh correlation
STDs: Time since last diagnosis is highly overall correlated with STDs and 4 other fieldsHigh correlation
Dx:Cancer is highly overall correlated with Dx:HPV and 1 other fieldsHigh correlation
Dx:CIN is highly overall correlated with DxHigh correlation
Dx:HPV is highly overall correlated with Dx:Cancer and 1 other fieldsHigh correlation
Dx is highly overall correlated with Dx:Cancer and 2 other fieldsHigh correlation
Hinselmann is highly overall correlated with Schiller and 1 other fieldsHigh correlation
Schiller is highly overall correlated with Hinselmann and 1 other fieldsHigh correlation
Biopsy is highly overall correlated with Hinselmann and 1 other fieldsHigh correlation
Smokes is highly imbalanced (55.7%)Imbalance
IUD (years) is highly imbalanced (70.5%)Imbalance
STDs (number) is highly imbalanced (57.7%)Imbalance
STDs:vaginal condylomatosis is highly imbalanced (63.5%)Imbalance
STDs:syphilis is highly imbalanced (57.2%)Imbalance
STDs:pelvic inflammatory disease is highly imbalanced (65.4%)Imbalance
STDs:genital herpes is highly imbalanced (65.4%)Imbalance
STDs:molluscum contagiosum is highly imbalanced (65.4%)Imbalance
STDs:HIV is highly imbalanced (57.2%)Imbalance
STDs:Hepatitis B is highly imbalanced (65.4%)Imbalance
STDs:HPV is highly imbalanced (64.7%)Imbalance
STDs: Number of diagnosis is highly imbalanced (78.2%)Imbalance
STDs: Time since first diagnosis is highly imbalanced (83.2%)Imbalance
STDs: Time since last diagnosis is highly imbalanced (83.4%)Imbalance
Dx:Cancer is highly imbalanced (85.3%)Imbalance
Dx:CIN is highly imbalanced (91.6%)Imbalance
Dx:HPV is highly imbalanced (85.3%)Imbalance
Dx is highly imbalanced (81.6%)Imbalance
Hinselmann is highly imbalanced (75.4%)Imbalance
Schiller is highly imbalanced (57.6%)Imbalance
Citology is highly imbalanced (70.8%)Imbalance
Biopsy is highly imbalanced (65.6%)Imbalance
Number of sexual partners has 26 (3.0%) zerosZeros
Num of pregnancies has 72 (8.4%) zerosZeros
Smokes (years) has 735 (85.7%) zerosZeros
Hormonal Contraceptives (years) has 377 (43.9%) zerosZeros

Reproduction

Analysis started2023-07-23 01:14:08.700549
Analysis finished2023-07-23 01:14:30.838090
Duration22.14 seconds
Software versionydata-profiling vv4.3.2
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct44
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.820513
Minimum13
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:30.985778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q120
median25
Q332
95-th percentile41
Maximum84
Range71
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.4979481
Coefficient of variation (CV)0.3168451
Kurtosis4.7785751
Mean26.820513
Median Absolute Deviation (MAD)5.5
Skewness1.3942788
Sum23012
Variance72.215121
MonotonicityNot monotonic
2023-07-22T19:14:31.274989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
23 54
 
6.3%
18 50
 
5.8%
21 46
 
5.4%
20 45
 
5.2%
19 44
 
5.1%
24 39
 
4.5%
25 39
 
4.5%
26 38
 
4.4%
28 37
 
4.3%
17 35
 
4.1%
Other values (34) 431
50.2%
ValueCountFrequency (%)
13 1
 
0.1%
14 5
 
0.6%
15 21
2.4%
16 23
2.7%
17 35
4.1%
18 50
5.8%
19 44
5.1%
20 45
5.2%
21 46
5.4%
22 30
3.5%
ValueCountFrequency (%)
84 1
0.1%
79 1
0.1%
70 2
0.2%
59 1
0.1%
52 2
0.2%
51 1
0.1%
50 1
0.1%
49 2
0.2%
48 2
0.2%
47 1
0.1%

Number of sexual partners
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.451049
Minimum0
Maximum28
Zeros26
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:31.534579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum28
Range28
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6985285
Coefficient of variation (CV)0.69298022
Kurtosis63.113561
Mean2.451049
Median Absolute Deviation (MAD)1
Skewness5.0420193
Sum2103
Variance2.8849989
MonotonicityNot monotonic
2023-07-22T19:14:31.737362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 272
31.7%
3 208
24.2%
1 206
24.0%
4 78
 
9.1%
5 44
 
5.1%
0 26
 
3.0%
6 9
 
1.0%
7 7
 
0.8%
8 4
 
0.5%
15 1
 
0.1%
Other values (3) 3
 
0.3%
ValueCountFrequency (%)
0 26
 
3.0%
1 206
24.0%
2 272
31.7%
3 208
24.2%
4 78
 
9.1%
5 44
 
5.1%
6 9
 
1.0%
7 7
 
0.8%
8 4
 
0.5%
9 1
 
0.1%
ValueCountFrequency (%)
28 1
 
0.1%
15 1
 
0.1%
10 1
 
0.1%
9 1
 
0.1%
8 4
 
0.5%
7 7
 
0.8%
6 9
 
1.0%
5 44
 
5.1%
4 78
 
9.1%
3 208
24.2%

First sexual intercourse
Real number (ℝ)

Distinct22
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.856643
Minimum0
Maximum32
Zeros7
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:31.971082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q115
median17
Q318
95-th percentile22
Maximum32
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1834914
Coefficient of variation (CV)0.18885678
Kurtosis8.0024548
Mean16.856643
Median Absolute Deviation (MAD)2
Skewness-0.055143674
Sum14463
Variance10.134617
MonotonicityNot monotonic
2023-07-22T19:14:32.238194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
15 163
19.0%
17 151
17.6%
18 137
16.0%
16 121
14.1%
14 79
9.2%
19 60
 
7.0%
20 37
 
4.3%
13 25
 
2.9%
21 20
 
2.3%
23 9
 
1.0%
Other values (12) 56
 
6.5%
ValueCountFrequency (%)
0 7
 
0.8%
10 2
 
0.2%
11 2
 
0.2%
12 6
 
0.7%
13 25
 
2.9%
14 79
9.2%
15 163
19.0%
16 121
14.1%
17 151
17.6%
18 137
16.0%
ValueCountFrequency (%)
32 1
 
0.1%
29 5
 
0.6%
28 3
 
0.3%
27 6
 
0.7%
26 7
 
0.8%
25 2
 
0.2%
24 6
 
0.7%
23 9
1.0%
22 9
1.0%
21 20
2.3%

Num of pregnancies
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1270396
Minimum0
Maximum11
Zeros72
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:32.478029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5081083
Coefficient of variation (CV)0.70901747
Kurtosis2.6997838
Mean2.1270396
Median Absolute Deviation (MAD)1
Skewness1.248135
Sum1825
Variance2.2743905
MonotonicityNot monotonic
2023-07-22T19:14:32.686255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 270
31.5%
2 240
28.0%
3 139
16.2%
4 74
 
8.6%
0 72
 
8.4%
5 35
 
4.1%
6 18
 
2.1%
7 6
 
0.7%
8 2
 
0.2%
11 1
 
0.1%
ValueCountFrequency (%)
0 72
 
8.4%
1 270
31.5%
2 240
28.0%
3 139
16.2%
4 74
 
8.6%
5 35
 
4.1%
6 18
 
2.1%
7 6
 
0.7%
8 2
 
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
11 1
 
0.1%
10 1
 
0.1%
8 2
 
0.2%
7 6
 
0.7%
6 18
 
2.1%
5 35
 
4.1%
4 74
 
8.6%
3 139
16.2%
2 240
28.0%
1 270
31.5%

Smokes
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
722 
1.0
123 
?
 
13

Length

Max length3
Median length3
Mean length2.969697
Min length1

Characters and Unicode

Total characters2548
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 722
84.1%
1.0 123
 
14.3%
? 13
 
1.5%

Length

2023-07-22T19:14:32.936615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:33.231391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 722
84.1%
1.0 123
 
14.3%
13
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 1567
61.5%
. 845
33.2%
1 123
 
4.8%
? 13
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1690
66.3%
Other Punctuation 858
33.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1567
92.7%
1 123
 
7.3%
Other Punctuation
ValueCountFrequency (%)
. 845
98.5%
? 13
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2548
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1567
61.5%
. 845
33.2%
1 123
 
4.8%
? 13
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1567
61.5%
. 845
33.2%
1 123
 
4.8%
? 13
 
0.5%

Smokes (years)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2012408
Minimum0
Maximum37
Zeros735
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:33.463920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile9.15
Maximum37
Range37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.0606225
Coefficient of variation (CV)3.3803569
Kurtosis24.187248
Mean1.2012408
Median Absolute Deviation (MAD)0
Skewness4.5037601
Sum1030.6646
Variance16.488655
MonotonicityNot monotonic
2023-07-22T19:14:33.738305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 735
85.7%
1.266972909 15
 
1.7%
5 9
 
1.0%
9 9
 
1.0%
1 8
 
0.9%
3 7
 
0.8%
2 7
 
0.8%
16 6
 
0.7%
7 6
 
0.7%
8 6
 
0.7%
Other values (20) 50
 
5.8%
ValueCountFrequency (%)
0 735
85.7%
0.16 1
 
0.1%
0.5 3
 
0.3%
1 8
 
0.9%
1.266972909 15
 
1.7%
2 7
 
0.8%
3 7
 
0.8%
4 5
 
0.6%
5 9
 
1.0%
6 4
 
0.5%
ValueCountFrequency (%)
37 1
 
0.1%
34 1
 
0.1%
32 1
 
0.1%
28 1
 
0.1%
24 1
 
0.1%
22 2
0.2%
21 1
 
0.1%
20 1
 
0.1%
19 3
0.3%
18 1
 
0.1%
Distinct63
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:34.033876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Length

Max length12
Median length3
Mean length3.2016317
Min length1

Characters and Unicode

Total characters2747
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)4.2%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row37.0
5th row0.0
ValueCountFrequency (%)
0.0 722
84.1%
0.5132021277 18
 
2.1%
13
 
1.5%
1.0 6
 
0.7%
3.0 5
 
0.6%
2.0 4
 
0.5%
0.75 4
 
0.5%
1.2 4
 
0.5%
0.2 4
 
0.5%
0.05 4
 
0.5%
Other values (53) 74
 
8.6%
2023-07-22T19:14:34.512717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1561
56.8%
. 845
30.8%
2 89
 
3.2%
1 69
 
2.5%
5 50
 
1.8%
7 50
 
1.8%
3 36
 
1.3%
? 13
 
0.5%
4 13
 
0.5%
6 10
 
0.4%
Other values (2) 11
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1889
68.8%
Other Punctuation 858
31.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1561
82.6%
2 89
 
4.7%
1 69
 
3.7%
5 50
 
2.6%
7 50
 
2.6%
3 36
 
1.9%
4 13
 
0.7%
6 10
 
0.5%
8 7
 
0.4%
9 4
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 845
98.5%
? 13
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2747
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1561
56.8%
. 845
30.8%
2 89
 
3.2%
1 69
 
2.5%
5 50
 
1.8%
7 50
 
1.8%
3 36
 
1.3%
? 13
 
0.5%
4 13
 
0.5%
6 10
 
0.4%
Other values (2) 11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1561
56.8%
. 845
30.8%
2 89
 
3.2%
1 69
 
2.5%
5 50
 
1.8%
7 50
 
1.8%
3 36
 
1.3%
? 13
 
0.5%
4 13
 
0.5%
6 10
 
0.4%
Other values (2) 11
 
0.4%

Hormonal Contraceptives
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
1.0
481 
0.0
269 
?
108 

Length

Max length3
Median length3
Mean length2.7482517
Min length1

Characters and Unicode

Total characters2358
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 481
56.1%
0.0 269
31.4%
? 108
 
12.6%

Length

2023-07-22T19:14:34.768080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:35.068946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 481
56.1%
0.0 269
31.4%
108
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 1019
43.2%
. 750
31.8%
1 481
20.4%
? 108
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1500
63.6%
Other Punctuation 858
36.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1019
67.9%
1 481
32.1%
Other Punctuation
ValueCountFrequency (%)
. 750
87.4%
? 108
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2358
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1019
43.2%
. 750
31.8%
1 481
20.4%
? 108
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2358
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1019
43.2%
. 750
31.8%
1 481
20.4%
? 108
 
4.6%

Hormonal Contraceptives (years)
Real number (ℝ)

ZEROS 

Distinct40
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9723944
Minimum0
Maximum30
Zeros377
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size6.8 KiB
2023-07-22T19:14:35.324260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.25
Q32
95-th percentile9
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.5978882
Coefficient of variation (CV)1.8241221
Kurtosis10.482291
Mean1.9723944
Median Absolute Deviation (MAD)0.25
Skewness2.8346558
Sum1692.3144
Variance12.944799
MonotonicityNot monotonic
2023-07-22T19:14:35.597922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 377
43.9%
1 77
 
9.0%
0.25 41
 
4.8%
2 40
 
4.7%
3 39
 
4.5%
5 34
 
4.0%
0.08 25
 
2.9%
0.5 25
 
2.9%
6 24
 
2.8%
4 22
 
2.6%
Other values (30) 154
17.9%
ValueCountFrequency (%)
0 377
43.9%
0.08 25
 
2.9%
0.16 16
 
1.9%
0.17 1
 
0.1%
0.25 41
 
4.8%
0.33 9
 
1.0%
0.41 1
 
0.1%
0.42 8
 
0.9%
0.5 25
 
2.9%
0.58 6
 
0.7%
ValueCountFrequency (%)
30 1
 
0.1%
22 1
 
0.1%
20 4
0.5%
19 2
 
0.2%
17 1
 
0.1%
16 2
 
0.2%
15 6
0.7%
14 2
 
0.2%
13 2
 
0.2%
12 4
0.5%

IUD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
658 
?
117 
1.0
83 

Length

Max length3
Median length3
Mean length2.7272727
Min length1

Characters and Unicode

Total characters2340
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 658
76.7%
? 117
 
13.6%
1.0 83
 
9.7%

Length

2023-07-22T19:14:35.896257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:36.206547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 658
76.7%
117
 
13.6%
1.0 83
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 1399
59.8%
. 741
31.7%
? 117
 
5.0%
1 83
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1482
63.3%
Other Punctuation 858
36.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1399
94.4%
1 83
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 741
86.4%
? 117
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2340
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1399
59.8%
. 741
31.7%
? 117
 
5.0%
1 83
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1399
59.8%
. 741
31.7%
? 117
 
5.0%
1 83
 
3.5%

IUD (years)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
658 
?
117 
3.0
 
11
2.0
 
10
5.0
 
9
Other values (22)
 
53

Length

Max length4
Median length3
Mean length2.7470862
Min length1

Characters and Unicode

Total characters2357
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)1.6%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 658
76.7%
? 117
 
13.6%
3.0 11
 
1.3%
2.0 10
 
1.2%
5.0 9
 
1.0%
1.0 8
 
0.9%
8.0 7
 
0.8%
7.0 7
 
0.8%
6.0 5
 
0.6%
4.0 5
 
0.6%
Other values (17) 21
 
2.4%

Length

2023-07-22T19:14:36.450311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.0 658
76.7%
117
 
13.6%
3.0 11
 
1.3%
2.0 10
 
1.2%
5.0 9
 
1.0%
1.0 8
 
0.9%
8.0 7
 
0.8%
7.0 7
 
0.8%
4.0 5
 
0.6%
6.0 5
 
0.6%
Other values (17) 21
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1401
59.4%
. 741
31.4%
? 117
 
5.0%
1 24
 
1.0%
5 15
 
0.6%
3 13
 
0.6%
2 12
 
0.5%
8 10
 
0.4%
7 9
 
0.4%
6 6
 
0.3%
Other values (2) 9
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1499
63.6%
Other Punctuation 858
36.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1401
93.5%
1 24
 
1.6%
5 15
 
1.0%
3 13
 
0.9%
2 12
 
0.8%
8 10
 
0.7%
7 9
 
0.6%
6 6
 
0.4%
4 6
 
0.4%
9 3
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 741
86.4%
? 117
 
13.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2357
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1401
59.4%
. 741
31.4%
? 117
 
5.0%
1 24
 
1.0%
5 15
 
0.6%
3 13
 
0.6%
2 12
 
0.5%
8 10
 
0.4%
7 9
 
0.4%
6 6
 
0.3%
Other values (2) 9
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2357
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1401
59.4%
. 741
31.4%
? 117
 
5.0%
1 24
 
1.0%
5 15
 
0.6%
3 13
 
0.6%
2 12
 
0.5%
8 10
 
0.4%
7 9
 
0.4%
6 6
 
0.3%
Other values (2) 9
 
0.4%

STDs
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
674 
?
105 
1.0
79 

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 674
78.6%
? 105
 
12.2%
1.0 79
 
9.2%

Length

2023-07-22T19:14:36.728221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:37.030523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 674
78.6%
105
 
12.2%
1.0 79
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 1427
60.4%
. 753
31.9%
? 105
 
4.4%
1 79
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1427
94.8%
1 79
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1427
60.4%
. 753
31.9%
? 105
 
4.4%
1 79
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1427
60.4%
. 753
31.9%
? 105
 
4.4%
1 79
 
3.3%

STDs (number)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
674 
?
105 
2.0
 
37
1.0
 
34
3.0
 
7

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 674
78.6%
? 105
 
12.2%
2.0 37
 
4.3%
1.0 34
 
4.0%
3.0 7
 
0.8%
4.0 1
 
0.1%

Length

2023-07-22T19:14:37.284044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:37.601890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 674
78.6%
105
 
12.2%
2.0 37
 
4.3%
1.0 34
 
4.0%
3.0 7
 
0.8%
4.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1427
60.4%
. 753
31.9%
? 105
 
4.4%
2 37
 
1.6%
1 34
 
1.4%
3 7
 
0.3%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1427
94.8%
2 37
 
2.5%
1 34
 
2.3%
3 7
 
0.5%
4 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1427
60.4%
. 753
31.9%
? 105
 
4.4%
2 37
 
1.6%
1 34
 
1.4%
3 7
 
0.3%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1427
60.4%
. 753
31.9%
? 105
 
4.4%
2 37
 
1.6%
1 34
 
1.4%
3 7
 
0.3%
4 1
 
< 0.1%

STDs:condylomatosis
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
709 
?
105 
1.0
 
44

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 709
82.6%
? 105
 
12.2%
1.0 44
 
5.1%

Length

2023-07-22T19:14:37.876048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:38.186383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 709
82.6%
105
 
12.2%
1.0 44
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 1462
61.8%
. 753
31.9%
? 105
 
4.4%
1 44
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1462
97.1%
1 44
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1462
61.8%
. 753
31.9%
? 105
 
4.4%
1 44
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1462
61.8%
. 753
31.9%
? 105
 
4.4%
1 44
 
1.9%

STDs:cervical condylomatosis
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
753 
?
105 

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 753
87.8%
? 105
 
12.2%

Length

2023-07-22T19:14:38.435681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:38.727070image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 753
87.8%
105
 
12.2%

Most occurring characters

ValueCountFrequency (%)
0 1506
63.7%
. 753
31.9%
? 105
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%
Decimal Number
ValueCountFrequency (%)
0 1506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1506
63.7%
. 753
31.9%
? 105
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1506
63.7%
. 753
31.9%
? 105
 
4.4%

STDs:vaginal condylomatosis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
749 
?
105 
1.0
 
4

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 749
87.3%
? 105
 
12.2%
1.0 4
 
0.5%

Length

2023-07-22T19:14:38.967217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:39.284561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 749
87.3%
105
 
12.2%
1.0 4
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 1502
63.5%
. 753
31.9%
? 105
 
4.4%
1 4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1502
99.7%
1 4
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1502
63.5%
. 753
31.9%
? 105
 
4.4%
1 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1502
63.5%
. 753
31.9%
? 105
 
4.4%
1 4
 
0.2%

STDs:vulvo-perineal condylomatosis
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
710 
?
105 
1.0
 
43

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 710
82.8%
? 105
 
12.2%
1.0 43
 
5.0%

Length

2023-07-22T19:14:39.534647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:39.832114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 710
82.8%
105
 
12.2%
1.0 43
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 1463
61.9%
. 753
31.9%
? 105
 
4.4%
1 43
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1463
97.1%
1 43
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1463
61.9%
. 753
31.9%
? 105
 
4.4%
1 43
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1463
61.9%
. 753
31.9%
? 105
 
4.4%
1 43
 
1.8%

STDs:syphilis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
735 
?
105 
1.0
 
18

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 735
85.7%
? 105
 
12.2%
1.0 18
 
2.1%

Length

2023-07-22T19:14:40.084143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:40.386120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 735
85.7%
105
 
12.2%
1.0 18
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1488
62.9%
. 753
31.9%
? 105
 
4.4%
1 18
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1488
98.8%
1 18
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1488
62.9%
. 753
31.9%
? 105
 
4.4%
1 18
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1488
62.9%
. 753
31.9%
? 105
 
4.4%
1 18
 
0.8%

STDs:pelvic inflammatory disease
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
752 
?
105 
1.0
 
1

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
? 105
 
12.2%
1.0 1
 
0.1%

Length

2023-07-22T19:14:40.636829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:40.944476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
87.6%
105
 
12.2%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1505
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

STDs:genital herpes
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
752 
?
105 
1.0
 
1

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
? 105
 
12.2%
1.0 1
 
0.1%

Length

2023-07-22T19:14:41.198990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:41.498093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
87.6%
105
 
12.2%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1505
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

STDs:molluscum contagiosum
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
752 
?
105 
1.0
 
1

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
? 105
 
12.2%
1.0 1
 
0.1%

Length

2023-07-22T19:14:41.744211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:42.044119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
87.6%
105
 
12.2%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1505
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

STDs:AIDS
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
753 
?
105 

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 753
87.8%
? 105
 
12.2%

Length

2023-07-22T19:14:42.298984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:42.588708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 753
87.8%
105
 
12.2%

Most occurring characters

ValueCountFrequency (%)
0 1506
63.7%
. 753
31.9%
? 105
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%
Decimal Number
ValueCountFrequency (%)
0 1506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1506
63.7%
. 753
31.9%
? 105
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1506
63.7%
. 753
31.9%
? 105
 
4.4%

STDs:HIV
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
735 
?
105 
1.0
 
18

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 735
85.7%
? 105
 
12.2%
1.0 18
 
2.1%

Length

2023-07-22T19:14:42.829969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:43.127331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 735
85.7%
105
 
12.2%
1.0 18
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 1488
62.9%
. 753
31.9%
? 105
 
4.4%
1 18
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1488
98.8%
1 18
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1488
62.9%
. 753
31.9%
? 105
 
4.4%
1 18
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1488
62.9%
. 753
31.9%
? 105
 
4.4%
1 18
 
0.8%

STDs:Hepatitis B
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
752 
?
105 
1.0
 
1

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 752
87.6%
? 105
 
12.2%
1.0 1
 
0.1%

Length

2023-07-22T19:14:43.373196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:43.676256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 752
87.6%
105
 
12.2%
1.0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1505
99.9%
1 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1505
63.7%
. 753
31.9%
? 105
 
4.4%
1 1
 
< 0.1%

STDs:HPV
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0.0
751 
?
105 
1.0
 
2

Length

Max length3
Median length3
Mean length2.7552448
Min length1

Characters and Unicode

Total characters2364
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 751
87.5%
? 105
 
12.2%
1.0 2
 
0.2%

Length

2023-07-22T19:14:43.925807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:44.229819image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 751
87.5%
105
 
12.2%
1.0 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 1504
63.6%
. 753
31.9%
? 105
 
4.4%
1 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1506
63.7%
Other Punctuation 858
36.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1504
99.9%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 753
87.8%
? 105
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2364
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1504
63.6%
. 753
31.9%
? 105
 
4.4%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1504
63.6%
. 753
31.9%
? 105
 
4.4%
1 2
 
0.1%

STDs: Number of diagnosis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
787 
1
 
68
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Length

2023-07-22T19:14:44.447222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:44.710865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 787
91.7%
1 68
 
7.9%
2 2
 
0.2%
3 1
 
0.1%

STDs: Time since first diagnosis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
?
787 
1.0
 
15
3.0
 
10
2.0
 
9
4.0
 
6
Other values (14)
 
31

Length

Max length4
Median length1
Mean length1.1829837
Min length1

Characters and Unicode

Total characters1015
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.7%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 787
91.7%
1.0 15
 
1.7%
3.0 10
 
1.2%
2.0 9
 
1.0%
4.0 6
 
0.7%
7.0 5
 
0.6%
16.0 4
 
0.5%
5.0 4
 
0.5%
6.0 3
 
0.3%
8.0 3
 
0.3%
Other values (9) 12
 
1.4%

Length

2023-07-22T19:14:44.952158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
787
91.7%
1.0 15
 
1.7%
3.0 10
 
1.2%
2.0 9
 
1.0%
4.0 6
 
0.7%
7.0 5
 
0.6%
16.0 4
 
0.5%
5.0 4
 
0.5%
8.0 3
 
0.3%
6.0 3
 
0.3%
Other values (9) 12
 
1.4%

Most occurring characters

ValueCountFrequency (%)
? 787
77.5%
0 72
 
7.1%
. 71
 
7.0%
1 31
 
3.1%
2 14
 
1.4%
3 10
 
1.0%
6 7
 
0.7%
4 6
 
0.6%
7 5
 
0.5%
5 5
 
0.5%
Other values (2) 7
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 858
84.5%
Decimal Number 157
 
15.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72
45.9%
1 31
19.7%
2 14
 
8.9%
3 10
 
6.4%
6 7
 
4.5%
4 6
 
3.8%
7 5
 
3.2%
5 5
 
3.2%
8 4
 
2.5%
9 3
 
1.9%
Other Punctuation
ValueCountFrequency (%)
? 787
91.7%
. 71
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1015
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
? 787
77.5%
0 72
 
7.1%
. 71
 
7.0%
1 31
 
3.1%
2 14
 
1.4%
3 10
 
1.0%
6 7
 
0.7%
4 6
 
0.6%
7 5
 
0.5%
5 5
 
0.5%
Other values (2) 7
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1015
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 787
77.5%
0 72
 
7.1%
. 71
 
7.0%
1 31
 
3.1%
2 14
 
1.4%
3 10
 
1.0%
6 7
 
0.7%
4 6
 
0.6%
7 5
 
0.5%
5 5
 
0.5%
Other values (2) 7
 
0.7%

STDs: Time since last diagnosis
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
?
787 
1.0
 
17
2.0
 
10
3.0
 
9
4.0
 
6
Other values (14)
 
29

Length

Max length4
Median length1
Mean length1.1818182
Min length1

Characters and Unicode

Total characters1014
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.8%

Sample

1st row?
2nd row?
3rd row?
4th row?
5th row?

Common Values

ValueCountFrequency (%)
? 787
91.7%
1.0 17
 
2.0%
2.0 10
 
1.2%
3.0 9
 
1.0%
4.0 6
 
0.7%
7.0 5
 
0.6%
16.0 4
 
0.5%
8.0 3
 
0.3%
6.0 3
 
0.3%
5.0 3
 
0.3%
Other values (9) 11
 
1.3%

Length

2023-07-22T19:14:45.206045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
787
91.7%
1.0 17
 
2.0%
2.0 10
 
1.2%
3.0 9
 
1.0%
4.0 6
 
0.7%
7.0 5
 
0.6%
16.0 4
 
0.5%
6.0 3
 
0.3%
5.0 3
 
0.3%
8.0 3
 
0.3%
Other values (9) 11
 
1.3%

Most occurring characters

ValueCountFrequency (%)
? 787
77.6%
0 72
 
7.1%
. 71
 
7.0%
1 32
 
3.2%
2 15
 
1.5%
3 9
 
0.9%
6 7
 
0.7%
4 6
 
0.6%
7 5
 
0.5%
8 4
 
0.4%
Other values (2) 6
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 858
84.6%
Decimal Number 156
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 72
46.2%
1 32
20.5%
2 15
 
9.6%
3 9
 
5.8%
6 7
 
4.5%
4 6
 
3.8%
7 5
 
3.2%
8 4
 
2.6%
5 4
 
2.6%
9 2
 
1.3%
Other Punctuation
ValueCountFrequency (%)
? 787
91.7%
. 71
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1014
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
? 787
77.6%
0 72
 
7.1%
. 71
 
7.0%
1 32
 
3.2%
2 15
 
1.5%
3 9
 
0.9%
6 7
 
0.7%
4 6
 
0.6%
7 5
 
0.5%
8 4
 
0.4%
Other values (2) 6
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 787
77.6%
0 72
 
7.1%
. 71
 
7.0%
1 32
 
3.2%
2 15
 
1.5%
3 9
 
0.9%
6 7
 
0.7%
4 6
 
0.6%
7 5
 
0.5%
8 4
 
0.4%
Other values (2) 6
 
0.6%

Dx:Cancer
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
840 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Length

2023-07-22T19:14:45.444121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:46.046891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Dx:CIN
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
849 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Length

2023-07-22T19:14:46.251397image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:46.499122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 849
99.0%
1 9
 
1.0%

Dx:HPV
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
840 
1
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Length

2023-07-22T19:14:46.768619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:47.019833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 840
97.9%
1 18
 
2.1%

Dx
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
834 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Length

2023-07-22T19:14:47.221394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:47.468575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 834
97.2%
1 24
 
2.8%

Hinselmann
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
823 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Length

2023-07-22T19:14:47.674330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:47.924626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring characters

ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 823
95.9%
1 35
 
4.1%

Schiller
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
784 
1
 
74

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Length

2023-07-22T19:14:48.125777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:48.379503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring characters

ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 784
91.4%
1 74
 
8.6%

Citology
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
814 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Length

2023-07-22T19:14:48.586588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:48.835123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 814
94.9%
1 44
 
5.1%

Biopsy
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.8 KiB
0
803 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters858
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Length

2023-07-22T19:14:49.038191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-22T19:14:49.292942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 858
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 858
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 858
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 803
93.6%
1 55
 
6.4%

Interactions

2023-07-22T19:14:27.047492image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:18.082572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:19.821888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:21.496862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:23.197869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:24.872802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:27.346120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:18.379870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:20.111291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:21.792368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:23.484182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:25.172368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:27.616634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:18.661308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:20.384176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:22.066109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:23.752820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:25.506830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:27.903433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:18.955236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:20.664066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:22.346192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:24.030162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:25.797039image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:28.178455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:19.236832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:20.930623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:22.623322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:24.302173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:26.080043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:28.533930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:19.534688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:21.221124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:22.914449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:24.592992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-22T19:14:26.757798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-22T19:14:49.640301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokes (years)Hormonal Contraceptives (years)SmokesHormonal ContraceptivesIUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy
Age1.0000.2030.4210.4620.0640.2720.0000.1500.1950.3650.0390.0000.0260.0890.0480.0300.0640.0340.0310.0220.0890.0320.0310.0380.0000.0000.0000.0830.3340.0750.2000.0000.1070.0000.056
Number of sexual partners0.2031.000-0.1020.1550.2380.0810.2610.0230.0330.0000.0730.0750.1110.0450.0000.1120.0180.0000.0000.0000.0450.0000.0000.0000.0320.2390.2410.0000.0000.0000.0000.0000.0000.0000.000
First sexual intercourse0.421-0.1021.0000.003-0.1310.1140.0470.0740.0330.0000.0570.0000.0650.1050.0990.0650.0770.0480.0480.0460.1050.0550.0480.0680.0000.0000.0000.0720.0000.0530.0570.0570.0550.0000.115
Num of pregnancies0.4620.1550.0031.0000.0490.2510.0250.1560.1620.0850.0640.0540.0630.0570.0000.0580.1560.0000.0000.0550.0570.0000.0000.0000.0000.0710.0790.0000.0000.0000.0000.0760.1010.0000.071
Smokes (years)0.0640.238-0.1310.0491.0000.0340.5530.0000.0260.0470.0860.0920.0000.0000.1900.0000.0000.0000.0000.0000.0000.1340.2030.0340.0510.1490.1540.1320.0000.1320.0000.0760.1310.0000.048
Hormonal Contraceptives (years)0.2720.0810.1140.2510.0341.0000.0000.2990.1650.3740.1090.0200.1030.1560.0910.1030.1160.0880.0880.0880.1560.0910.0880.1350.0000.0000.0000.1080.0000.1080.0000.1440.1650.1720.196
Smokes0.0000.2610.0470.0250.5530.0001.0000.0600.0420.0000.0870.0970.0480.0000.0320.0500.0570.0000.0000.0000.0000.0400.0460.0000.1500.1960.1580.0120.0000.0130.0600.0000.0380.0000.000
Hormonal Contraceptives0.1500.0230.0740.1560.0000.2990.0601.0000.6530.6440.6210.6200.6190.8760.6200.6190.6200.6190.6190.6200.8760.6220.6200.6200.0000.0000.0000.0380.0000.0090.0000.0700.1060.0310.087
IUD0.1950.0330.0330.1620.0260.1650.0420.6531.0000.9860.6370.6380.6370.8980.6350.6360.6350.6340.6340.6340.8980.6360.6340.6340.0000.0960.1130.1150.0620.0470.1310.0620.1200.0000.065
IUD (years)0.3650.0000.0000.0850.0470.3740.0000.6440.9861.0000.6280.3880.6370.8820.6240.6370.6140.6120.6120.6120.8820.6180.6120.6120.0000.1230.1730.1420.3090.0810.2210.2010.2270.0000.055
STDs0.0390.0730.0570.0640.0860.1090.0870.6210.6370.6281.0000.9980.8740.9990.7220.8710.7770.7100.7100.7100.9990.7770.7100.7140.6650.6520.6520.0260.0000.0260.0000.0770.1390.0400.117
STDs (number)0.0000.0750.0000.0540.0920.0200.0970.6200.6380.3880.9981.0000.9920.9980.8110.9860.8550.7140.7140.7140.9980.8310.7130.7240.8450.5130.4810.0310.0000.0310.0000.1710.1650.0000.104
STDs:condylomatosis0.0260.1110.0650.0630.0000.1030.0480.6190.6370.6370.8740.9921.0000.9990.7360.9940.7070.7060.7060.7060.9990.7110.7060.7060.4990.5330.5320.0470.0000.0470.0360.0800.1410.0550.098
STDs:cervical condylomatosis0.0890.0450.1050.0570.0000.1560.0000.8760.8980.8820.9990.9980.9991.0000.9990.9990.9990.9990.9990.9990.9950.9990.9990.9990.0950.0000.0000.0250.0000.0250.0000.0590.0890.0000.051
STDs:vaginal condylomatosis0.0480.0000.0990.0000.1900.0910.0320.6200.6350.6240.7220.8110.7360.9991.0000.7230.7060.7060.7060.7060.9990.7060.7060.7060.1720.2350.2230.0280.0000.0280.0000.0620.0930.0000.053
STDs:vulvo-perineal condylomatosis0.0300.1120.0650.0580.0000.1030.0500.6190.6360.6370.8710.9860.9940.9990.7231.0000.7070.7060.7060.7060.9990.7110.7060.7060.4920.5270.5240.0460.0000.0460.0350.0810.1430.0580.100
STDs:syphilis0.0640.0180.0770.1560.0000.1160.0570.6200.6350.6140.7770.8550.7070.9990.7060.7071.0000.7060.7060.7060.9990.7070.7060.7060.3170.3830.3930.0350.0000.0350.0120.0600.0900.0210.065
STDs:pelvic inflammatory disease0.0340.0000.0480.0000.0000.0880.0000.6190.6340.6120.7100.7140.7060.9990.7060.7060.7061.0000.7060.7060.9990.7060.7060.7060.0970.4850.4850.0260.0000.0260.0000.0610.0910.0000.050
STDs:genital herpes0.0310.0000.0480.0000.0000.0880.0000.6190.6340.6120.7100.7140.7060.9990.7060.7060.7060.7061.0000.7060.9990.7060.7060.7060.0970.1340.1180.0260.0000.0260.0000.0610.0910.0000.139
STDs:molluscum contagiosum0.0220.0000.0460.0550.0000.0880.0000.6200.6340.6120.7100.7140.7060.9990.7060.7060.7060.7060.7061.0000.9990.7060.7060.7060.0970.1340.1180.0260.0000.0260.0000.0610.0910.0000.050
STDs:AIDS0.0890.0450.1050.0570.0000.1560.0000.8760.8980.8820.9990.9980.9990.9950.9990.9990.9990.9990.9990.9991.0000.9990.9990.9990.0950.0000.0000.0250.0000.0250.0000.0590.0890.0000.051
STDs:HIV0.0320.0000.0550.0000.1340.0910.0400.6220.6360.6180.7770.8310.7110.9990.7060.7110.7070.7060.7060.7060.9991.0000.7250.7060.4110.4600.4440.0350.0430.0350.0000.1070.1530.0690.133
STDs:Hepatitis B0.0310.0000.0480.0000.2030.0880.0460.6200.6340.6120.7100.7130.7060.9990.7060.7060.7060.7060.7060.7060.9990.7251.0000.7060.0970.4850.4850.0260.0000.0260.0000.0610.0910.0000.050
STDs:HPV0.0380.0000.0680.0000.0340.1350.0000.6200.6340.6120.7140.7240.7060.9990.7060.7060.7060.7060.7060.7060.9990.7060.7061.0000.0740.2170.2170.3300.0000.3300.1360.0610.0910.0000.051
STDs: Number of diagnosis0.0000.0320.0000.0000.0510.0000.1500.0000.0000.0000.6650.8450.4990.0950.1720.4920.3170.0970.0970.0970.0950.4110.0970.0741.0000.6800.5890.0000.0000.0000.0000.1580.1460.0370.102
STDs: Time since first diagnosis0.0000.2390.0000.0710.1490.0000.1960.0000.0960.1230.6520.5130.5330.0000.2350.5270.3830.4850.1340.1340.0000.4600.4850.2170.6801.0000.9710.0000.3000.0000.1740.0000.1730.0980.145
STDs: Time since last diagnosis0.0000.2410.0000.0790.1540.0000.1580.0000.1130.1730.6520.4810.5320.0000.2230.5240.3930.4850.1180.1180.0000.4440.4850.2170.5890.9711.0000.0000.3000.0000.1740.0000.1680.0960.142
Dx:Cancer0.0830.0000.0720.0000.1320.1080.0120.0380.1150.1420.0260.0310.0470.0250.0280.0460.0350.0260.0260.0260.0250.0350.0260.3300.0000.0000.0001.0000.0000.8580.6400.1090.1390.0890.140
Dx:CIN0.3340.0000.0000.0000.0000.0000.0000.0000.0620.3090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0430.0000.0000.0000.3000.3000.0001.0000.0000.5720.0000.0000.0000.083
Dx:HPV0.0750.0000.0530.0000.1320.1080.0130.0090.0470.0810.0260.0310.0470.0250.0280.0460.0350.0260.0260.0260.0250.0350.0260.3300.0000.0000.0000.8580.0001.0000.5910.1090.1390.0890.140
Dx0.2000.0000.0570.0000.0000.0000.0600.0000.1310.2210.0000.0000.0360.0000.0000.0350.0120.0000.0000.0000.0000.0000.0000.1360.0000.1740.1740.6400.5720.5911.0000.0420.0790.0640.139
Hinselmann0.0000.0000.0570.0760.0760.1440.0000.0700.0620.2010.0770.1710.0800.0590.0620.0810.0600.0610.0610.0610.0590.1070.0610.0610.1580.0000.0000.1090.0000.1090.0421.0000.6390.1760.535
Schiller0.1070.0000.0550.1010.1310.1650.0380.1060.1200.2270.1390.1650.1410.0890.0930.1430.0900.0910.0910.0910.0890.1530.0910.0910.1460.1730.1680.1390.0000.1390.0790.6391.0000.3510.724
Citology0.0000.0000.0000.0000.0000.1720.0000.0310.0000.0000.0400.0000.0550.0000.0000.0580.0210.0000.0000.0000.0000.0690.0000.0000.0370.0980.0960.0890.0000.0890.0640.1760.3511.0000.315
Biopsy0.0560.0000.1150.0710.0480.1960.0000.0870.0650.0550.1170.1040.0980.0510.0530.1000.0650.0500.1390.0500.0510.1330.0500.0510.1020.1450.1420.1400.0830.1400.1390.5350.7240.3151.000

Missing values

2023-07-22T19:14:29.144927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-22T19:14:30.391737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy
0184.015.01.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
1151.014.01.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
2341.00.01.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
3525.016.04.01.037.00000037.01.03.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??10100000
4463.021.04.00.00.0000000.01.015.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
5423.023.02.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
6513.017.06.01.034.0000003.40.00.01.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00001101
7261.026.03.00.00.0000000.01.02.01.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
8451.020.05.00.00.0000000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??10110000
9443.015.00.01.01.2669732.80.00.0??0.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy
848313.018.01.00.00.00.01.00.500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
849323.018.01.01.011.00.161.06.000.00.01.01.00.00.00.00.00.00.00.00.00.00.00.01.00??10100000
850191.014.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
851232.015.02.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
852433.017.03.00.00.00.01.05.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
853343.018.00.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
854322.019.01.00.00.00.01.08.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
855252.017.00.00.00.00.01.00.080.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000010
856332.024.02.00.00.00.01.00.080.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000
857292.020.01.00.00.00.01.00.500.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??00000000

Duplicate rows

Most frequently occurring

AgeNumber of sexual partnersFirst sexual intercourseNum of pregnanciesSmokesSmokes (years)Smokes (packs/year)Hormonal ContraceptivesHormonal Contraceptives (years)IUDIUD (years)STDsSTDs (number)STDs:condylomatosisSTDs:cervical condylomatosisSTDs:vaginal condylomatosisSTDs:vulvo-perineal condylomatosisSTDs:syphilisSTDs:pelvic inflammatory diseaseSTDs:genital herpesSTDs:molluscum contagiosumSTDs:AIDSSTDs:HIVSTDs:Hepatitis BSTDs:HPVSTDs: Number of diagnosisSTDs: Time since first diagnosisSTDs: Time since last diagnosisDx:CancerDx:CINDx:HPVDxHinselmannSchillerCitologyBiopsy# duplicates
0151.014.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000004
7172.015.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000003
1151.015.01.00.00.00.0?0.00????????????????0??000000002
2152.014.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000002
3161.014.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000002
4161.015.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000002
5171.016.01.00.00.00.0?0.00????????????????0??000000002
6171.017.01.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000002
8172.015.01.00.00.00.01.00.330.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000002
9181.014.02.00.00.00.00.00.000.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00??000000002